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2016 | OriginalPaper | Buchkapitel

Distributed Big Data Techniques for Health Sensor Information Processing

verfasst von : Diego Gachet, María de la Luz Morales, Manuel de Buenaga, Enrique Puertas, Rafael Muñoz

Erschienen in: Ubiquitous Computing and Ambient Intelligence

Verlag: Springer International Publishing

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Abstract

Recent advances in wireless sensors technology applied to e-health allow the development of “personal medicine” concept, whose main goal is to identify specific therapies that make safe and effective individualized treatment of patients based, for example, in health status remote monitoring. Also the existence of multiple sensor devices in Hospital Units like ICUs (Intensive Care Units) constitute a big source of data, increasing the volume of health information to be analyzed in order to detect or predict abnormal situations in patients. In order to process this huge volume of information it is necessary to use Big Data and IoT technologies. In this paper, we present a general approach for sensor’s information processing and analysis based on Big Data concepts and to describe the use of common tools and techniques for storing, filtering and processing data coming from sensors in an ICU using a distributed architecture based on cloud computing. The proposed system has been developed around Big Data paradigms using bio-signals sensors information and machine learning algorithms for prediction of outcomes.

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Metadaten
Titel
Distributed Big Data Techniques for Health Sensor Information Processing
verfasst von
Diego Gachet
María de la Luz Morales
Manuel de Buenaga
Enrique Puertas
Rafael Muñoz
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-48746-5_22